short-paper

Fostering Serendipitous Knowledge Discovery using an Adaptive Multigraph-based Faceted Browser

Authors:

Peter van den Besselaar,

Klaas Andries de GraafAuthors Info & Claims

K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

Article No.: 15, Pages 1 - 4

https://doi.org/10.1145/3148011.3148037

Published: 04 December 2017 Publication History

Abstract

Serendipity, the art of making an unsought finding plays also an important role in the emerging field of data science, allowing the discovery of interesting and valuable facts not initially sought for. Previous research has extracted many serendipity-fostering patterns applicable to digital data-driven systems. Linked Open Data (LOD) on the Web which is powered by the Follow-Your-Nose effect, provides already a rich source for serendipity. The serendipity most often takes place when browsing data. Therefore, flexible and intuitive browsing user interfaces which support serendipity triggers such as enigmas, anomalies and novelties, can increase the likelihood of serendipity on LOD. In this work, we propose a set of serendipity-fostering design features supported by an adaptive multigraph-based faceted browsing interface to catalyze serendipity on Semantic Web and LOD environments.

References

[1]

A. Acosta. Using serendipity to advance knowledge building activities. Ontario Institute for Studies in Education, University of Toronto, Canada, 2012.

[2]

G. Alemu, B. Stevens, P. Ross, and J. Chandler. Linked data for libraries: Benefits of a conceptual shift from library-specific record structures to rdf-based data models. New Library World, 113(11/12):549--570, 2012.

[3]

L. Björneborn. Design dimensions enabling divergent behaviour across physical, digital, and social library interfaces. In PERSUASIVE. Springer, 2010.

Digital Library

[4]

F. Bobillo, M. Delgado, and J. Gómez-Romero. An ontology design pattern for representing relevance in owl. The Semantic Web, pages 72--85, 2007.

Digital Library

[5]

P. H. Cleverley and S. Burnett. Retrieving haystacks: a data driven information needs model for faceted search. Journal of Information Science, 41(1), 2015.

Digital Library

[6]

M. Cunha. Serendipity: Why some organizations are luckier than others. Universidade Nova de Lisboa (Ed.), FEUNL Working Paper Series, 2005.

[7]

E. Hargrave-Thomas, B. Yu, and J. Reynisson. The effect of serendipity in drug discovery and development. Chemistry in New Zealand, 2012.

[8]

J. Heinström. Psychological factors behind incidental information acquisition. Library & Information Science Research, 28(4):579--594, 2007.

[9]

D. Karger and M. Schraefel. The pathetic fallacy of rdf. SWUI, 2006.

[10]

A. Khalili and K. A. de Graaf. Linked data reactor: Towards data-aware user interfaces. In SEMANTiCS 2017. ACM, 2017.

Digital Library

[11]

A. Khalili, A. Loizou, and F. van Harmelen. Adaptive linked data-driven web components: Building flexible and reusable semantic web interfaces. InESWC2016, pages 677--692, 2016.

Digital Library

[12]

A. Khalili, P. van Andel, P. van den Besselaar, and K. A. de Graaf. The three princess of serendip on linked data, 2017. http://research.ld-r.org/papers/three-pincess-LinkedData.pdf.

[13]

H. Liu, Y. Wu, and YimingYang. Analogical inference for multi-relational embeddings. https://arxiv.org/abs/1705.02426, 2017.

[14]

G. Marchionini. Exploratory search: from finding to understanding. Commun. ACM, 49(4):41--46, Apr. 2006.

Digital Library

[15]

R. Merton and E. Barber. The Travels and Adventures of Serendipity: A Study in Sociological Semantics and the Sociology of Science. Princeton Press, 2004.

[16]

D. A. Norman. Living with complexity. MIT press, 2010.

[17]

D. A. Norman. The Design of Everyday Things: Revised and Expanded Edition. Basic Books, Inc., New York, NY, USA, 2013.

[18]

N. Ramakrishnan and A. Y. Grama. Data mining: From serendipity to science. Computer, 32(8):34--37, 1999.

Digital Library

[19]

G. M. Sacco and Y. Tzitzikas. Dynamic taxonomies and faceted search: theory, practice, and experience, volume 25. Springer Science & Business Media, 2009.

Digital Library

[20]

N. Shadbolt, T. Berners-Lee, and W. Hall. The semantic web revisited. IEEE intelligent systems, 21(3):96--101, 2006.

Digital Library

[21]

B. Shneiderman and C. Plaisant. Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In AVI2006 workshop, pages 1--7. ACM, 2006.

Digital Library

[22]

V. C. Storey, V. Sugumaran, and A. Burton-Jones. The role of user profiles in context-aware query processing for the semantic web. In Intl. Conf. on Application of Natural Language to Information Systems, pages 51--63, 2004.

[23]

E. G. Toms et al. Serendipitous information retrieval. In DELOS Workshop, pages 17--20, 2000.

[24]

P. van Andel. Anatomy of the unsought finding. serendipity: Origin, history, domains, traditions, appearances, patterns and programmability. Br J Philos Sci, 45(2):631--648, June 1994.

[25]

P. van Andel and D. Bourcier. Serendipity & abduction in proofs, presumptions & emerging laws. In The Dynamics of Judicial Proof, pages 273--286. 2002.

[26]

L. Yu. Follow Your Nose: A Basic Semantic Web Agent, pages 711--736. Springer Berlin Heidelberg, Berlin, Heidelberg, 2014.

Cited By

Khan ARashid UAhmed N(2023)AMED: Aggregated multimedia exploratory and discovery search softwareSoftwareX10.1016/j.softx.2023.10131221(101312)Online publication date: Feb-2023
https://doi.org/10.1016/j.softx.2023.101312
Khan ARashid UAhmed N(2022)An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web ContentIEEE Access10.1109/ACCESS.2022.317759710(56316-56330)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3177597
Sellami SZarour N(2022)Keyword-based faceted search interface for knowledge graph construction and explorationInternational Journal of Web Information Systems10.1108/IJWIS-02-2022-003718:5/6(453-486)Online publication date: 25-Oct-2022
https://doi.org/10.1108/IJWIS-02-2022-0037
Show More Cited By

Index Terms

Fostering Serendipitous Knowledge Discovery using an Adaptive Multigraph-based Faceted Browser
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning

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cover image ACM Conferences

K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

December 2017

271 pages

ISBN:9781450355537

DOI:10.1145/3148011

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 December 2017

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K-CAP 2017

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SIGAI

K-CAP 2017: Knowledge Capture Conference

December 4 - 6, 2017

TX, Austin, USA

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Khan ARashid UAhmed N(2023)AMED: Aggregated multimedia exploratory and discovery search softwareSoftwareX10.1016/j.softx.2023.10131221(101312)Online publication date: Feb-2023
https://doi.org/10.1016/j.softx.2023.101312
Khan ARashid UAhmed N(2022)An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web ContentIEEE Access10.1109/ACCESS.2022.317759710(56316-56330)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3177597
Sellami SZarour N(2022)Keyword-based faceted search interface for knowledge graph construction and explorationInternational Journal of Web Information Systems10.1108/IJWIS-02-2022-003718:5/6(453-486)Online publication date: 25-Oct-2022
https://doi.org/10.1108/IJWIS-02-2022-0037
Allen CErdelez S(2019)Distraction to illumination: Mining biomedical publications for serendipity in researchProceedings of the Association for Information Science and Technology10.1002/pra2.2018.1450550100355:1(10-18)Online publication date: Feb-2019
https://doi.org/10.1002/pra2.2018.14505501003
Khalili Avan den Besselaar Pde Graaf K(2018)FERASAT: A Serendipity-Fostering Faceted Browser for Linked DataThe Semantic Web10.1007/978-3-319-93417-4_23(351-366)Online publication date: 3-Jun-2018
https://doi.org/10.1007/978-3-319-93417-4_23

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